The authors of Omniglot developed a generative probabilistic model called Bayesian Program Learning (BPL)https://github.com/brendenlake/BPL, as a non-neural baseline for Omniglot tasks.
BPL has held the state of the art for the most challenging setup: the 20-way one-shot classification task without the use of data augmentations during training, it also generalizes very well from the Minimal training set
(see Table REF ).
The authors argue [1]} that the challenge remains unsolved because the human level on the within-alphabet classification has been out of reach for deep learning models that do not rely on heavy data augmentation.
Most of the published research on Omniglot classification addresses a simpler between-alphabets classification task, in which testing episodes contain characters from different alphabets, making the examples easier to distinguish.
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